Multivariable Predictive Control (MPC) is the most widely used advanced process control technology in process industries, with more than 5,000 worldwide applications currently in service. MPC, which is sometimes also referred to as multivariate control (MVC), relies on dynamic models of an underlying process, e.g., linear dynamic models obtained by system identification.
A common and challenging problem is that MPC control performance degrades over time due to inevitable changes in the underlying subject process, such as equipment modifications, changes in operating strategy, feed composition changes, instrumentation degradation, etc. Such degradation of control performance results in loss of benefits. Among all possible causes of control performance degradation, the poor model accuracy is the primary factor in most cases. To sustain good control performance, the model needs to be periodically calibrated and updated.
However, it is a technically challenging and resource-intensive task to pinpoint a problematic model and re-identify a new model for replacement in a MPC application. Such efforts are disclosed in Assignee's U.S. Pat. Nos. 7,209,793 and 6,819,964. In a large scale MPC application, over a hundred variables may be involved. Conducting a re-test and re-identification by a conventional approach may take an experienced engineer weeks of intensive work and cause significant interruption to the normal operation.
The process industry has been looking for an automated, safe and less invasive approach to conducting plant testing for a while. In past generations of MPC's, a process control engine calculated a new steady state target every single control cycle, no matter how insignificant the increment change was. The new steady state target was then used to calculate a new dynamic move plan, and the first move was written out to the subject process. Doing so guaranteed that the process controller had an integration action that removed offset from the active CV (controlled variable) limits that were caused by measurement noise, process disturbances, and model errors; but this often caused unwarranted overreaction to plant noise and the near colinearity present in the controller model. This lead to excessive feedback correlation in collected data sets making this closed loop data set unsuitable for model identification purposes. If purely closed loop data with the MPC system turned on was used, then the identified models were significantly biased (i.e., contained large errors). Often, the models identified from purely closed loop data were not sufficiently accurate to drive the correct behaviour in a non-square Multivariable Predictive Controller.
In the Assignee's U.S. Pat. No. 7,209,793, herein incorporated by reference, an innovative approach for conducting plant testing was proposed and improved on closed loop step testing of U.S. Pat. No. 6,819,964 also by Assignee and herein incorporated by reference. The step testing was done in an automated way with the process safe guarded by a specially designed multivariable controller. A process perturbation approach simultaneously perturbed multiple process input variables in a manner that maximized process outputs while maintaining process variables inside predefined operating constraints.
However, even using the above automated closed-loop step testing, the variable perturbation will inevitably have some negative impact to the normal process operation; mainly, the loss of optimal operation performance. As such, the currently available technology in this field is still considered too invasive to the process operation.
The innovation presented by Applicants below is geared to address this issue. Applicants provide a new apparatus and method for non-invasive closed loop step testing using a tunable trade-off between optimal process operation and process perturbation.